The present invention relates to an automated collection system for monitoring customer accounts, identifying delinquent, overlimit or special status accounts, and automatically initiating collection activities including sending electronic messages, such as e-mail, and populating associated web pages allowing electronic responses. The present invention also enables an automatic testing system to analyze the effectiveness of the various collection activities and modify system behavior based on the analysis.
Software-based collection systems, such as the AMS CACS® system (described below) manage a series of activities related to recovering outstanding debts from customers. The collection system receives the customer segment from the central decision engine, and the general strategy to be deployed. The collection system then manages the exact workflow, processes and interactions between the institution and the customer.
Computer assisted credit management systems, including computer assisted collections, are known in the art. Generally, a collection system includes a variety of components, such as a Collection Engine, a Decision Engine, a User Interface (for either a collector or customer), and other components. A collector is a user of a collection system whose primary job is to use a collection system to facilitate collecting payments on accounts needing collection action, such as delinquent, overlimit or special status accounts, overlimit accounts, special status accounts, etc. Collection systems generally include parameters, such as collection policy parameters. Collection policy parameters are used by credit granting institutions to specify how a collection system implements the collection policy of the credit granting institution.
Examples of computer assisted collections systems include the Computer Assisted Collection System (or CACS®), by American Management Systems, Inc (AMS®), and its several versions including CACS Enterprise, Computer Assisted Collection System for Government (including TRACE), and CACS-Telecom. CACS Enterprise is explained in a document available from AMS entitled CACS Enterprise Product Profile, March 1998 by AMS, incorporated herein by reference. CACS Enterprise, currently at version 7.0, is a member of the AMS series of credit management software that supports all phases of credit operations, from initial application processing through servicing and accounting to collection. Each system can be installed individually, collectively, or in any combination to address evolving support requirements.
A Computer Assisted Collection System for Government is explained in CACSPlus Product Profile (Client/Server version), August 1997, available from AMS, and incorporated herein by reference. The CACS-Telecom product is explained in CACS-Telecom Product Profile, September 1998, available from AMS and incorporated herein by reference. In addition, there are mainframe versions of CACS having a 3270 interface thereto, such as TRACE.
Software-based collection systems use a variety of commercially available software-based decision management tools, such as the STRATA® system from American Management Systems. STRATA applies predictive modeling techniques to customer data.
Automated test and learn systems such as the Rapid Strategy Evolution™ (RSE) may be used by computer assisted collection systems to identify the most effective offer from test and control strategies, and allow the creation of hybrid collection strategies that may be more effective than any of the individual tests. RSE Further advances controlled testing by tracking impact on specific test group segments, and supports immediate identification of the strongest and weakest elements within an overall strategy. Rapid Strategy Evolution is explained in such documents as Customer Decision Strategies, available from AMS and based on a presentation of the Jul. 31, 1997 Consumer Value Management Seminar.
Applicants also note that an advertised system SolveMyDebt.com may provide further background material. The system (not available) is described as a debtor initiated Internet collection system, which can interface with current collection and servicing systems to facilitate collections. Capabilities claimed include ability to view bills and debts over the Internet.
Current software-based collection systems manage a series of activities related to collecting outstanding debts from customers. Known collection systems generally receive account decisions, from a decision support engine, such as account limits and suggest contact messages to be sent to the customer, as well as the method of communication. Known collection systems feed account decisions, contact messages and methods to mailing systems for direct mail or people for telephone delivery.
Current commercially available software-based decision management systems apply predictive modeling techniques to customer data. Such a software-based decision engine may receive information from operational and/or customer information systems and data warehouses. This information is used to prioritize and tailor customer interactions based on predictive information, specific business rules, and continually evolving decision strategies. The decision engine then determines an appropriate action that is likely to be taken by a receivables system.
Currently, the appropriate actions are limited to direct mailings, in-person calls, recorded message delivery and one way electronic alerts such as e-mails. Typically, collection decisions are sent to customer contact representatives, or human agents who interact with customers about past due payments. A representative from the biller, bill consolidator, or third party will contact customers with delinquencies, overlimits or special status, to remind them of late payments, set up payment schedules, or receive promises to pay, or make other agreements. The customer may respond to the telephone contact by sending payment via direct mail or using electronic transfer, but such action cannot be completed over the telephone. The exception would be a bank calling regarding an overdraft on an account where the customer has multiple accounts with that institution and has the ability to transfer funds over the telephone. The contact is not customer-driven, and there is no capability for the customer to respond proactively to resolve the situation by completing, or arranging for, a payment through the interaction.
There are several varieties of electronic alerts currently available, but they are not self-driven, and there is no proactive response capability built in. For example, PCS devices offer weather, news and sports alerts via text messaging, and certain credit card companies currently send e-mail messages to remind customers of delinquent payments, overlimits or special status and upcoming penalties if the matter is not resolved. Certain banks provide triggered information alerts, or e-mail/fax notification if account balances fall below a certain level. However, once the notification is of a problem is received, the customer must use another channel to correct the situation, such as mail, dial-up, telephone or Internet banking.
FIG. 1 is a block diagram of a typical known automated receivables management system. A plurality of external systems 10 (such as an accounting systems, billing systems, credit bureau systems and data warehouses) monitor a data warehouse 12 and initiate a trigger when certain conditions are met. Such events may be automatically generated due to customer behavior or systematically produced at specified time intervals (i.e., monthly). Examples of events include a credit account delinquency, overlimit, statement cycle date, bounced check, or customer declaring bankruptcy. These external systems 10 pass the triggers and/or related data to a report database 14 and/or a decision engine 16. The report data base 14 can produce a variety of reports 20.
The decision engine 16, for example, the AMS STRATA™ system, applies predictive modeling techniques to customer data. The decision engine 16 receives information from the external systems 10 and outputs prioritized and tailored customer interactions based on predictive information, specific business rules, and continually evolving decision strategies. The decision engine 16 then determines appropriate actions to be taken by a collections system 18. The collection system 18 interfaces with a customer contact workstation 22 to carry out an appropriate collection action. An appropriate collection action may include, a specific collections procedure such as a late payment notice sent via regular mail and/or an agent-2Q assisted customer contact process facilitated by the customer contact workstation 22. The customer's response, either through the mail or via an agent-assisted customer contact process results in data being fed back to the data warehouse 12.
First, the decision engine 16 assigns a customer to a segment. A segment is a grouping of customers based on a set of characteristics to represent different objectives for the institution (grow, manage risk, prevent attrition). Generally, a segment is a high level segregation of customer for the purpose of associating a largely independent high level strategy. Based on objectives, a unique set of valuation and subsequent strategies are defined for each segment. For example, a collection system might segregate by product, having a segment for customers that have a late credit account payment and another for customers that have a delinquent mortgage payment.
Customer segments are then randomly divided into a control group and one or more test groups for the purpose of applying competing policy rules, strategies or experiments. Generally, test groups allow for strategy comparison. Just as in research environments, the behavior or outcomes of an experimental “test” population is compared to that of a “control” group not exposed to the experimental treatment. A strategist can specify what percentage of the customers should be randomly assigned to each test group. If the strategy associated with a test group is successful, that strategy may later be deployed to a larger percentage of the customers. Data regarding past tests and results is manually collected and fed back into the decision engine 16.
After customers are segmented and assigned to test/control groups, inbound events are matched to treatments to be used by the collection system 18. More specifically, the treatments to be invoked by each inbound event are defined. For example, different treatments are created for delinquent credit cards versus delinquent mortgages. This decisioning is performed using decision engine tables with defined treatment sequences, such as product type, amount of debt, length of delinquency, or value of the customer relationship. The order of treatment execution is also specified. For example, customers are divided into segments by product (credit card and mortgage), then by length of delinquency and then by customer value. Credit customers with delinquencies of more than 60 days, with low customer value will be sent to external collection agencies for processing. Credit customers with delinquencies of less than 60 days and high customer value will be called by a customer service representative to initiate payment arrangements.
Known collections systems 18, such as the AMS CACS™ product, manage a series of activities related to recovering outstanding debts from customers. The collection system 18 receives the customer segment and general strategy to be deployed from the decision engine 16.
The collection system then manages the exact workflow, processes and interactions between the institution and the customer. The collections system 18 feeds account decisions, contact messages and methods to queues associated with systems for direct mail or people for direct telephone delivery of the collections message. All customer contact, customer decisions and collections information is captured in the collection system and/or the data warehouse.
FIG. 2 is a flowchart of a known collection processes. In step S1 a trigger is issued by one of the external systems 10. Thereafter, in step S2, a customer strategy is determined by the decision engine 16 based on the trigger and associated information. Next, in step S3, a customer segment is identified. In step S4 customers are divided into control and test groups. Please note that this step is entirely optional.
The customers in the test and control groups are processed in step S5 with the formatting and delivery of a message to the customer. The messages can be given either by a direct mailing or by telephonic contact with a customer representative. In the event the customer does not respond, as in step S6, the process returns to step S2 for further consideration of strategy. However, the message results, hopefully, in customer interaction with a representative in step S7. In conjunction therewith, a record of the contact is made in step S8.
The customer will potentially make some promise of payment or actual payment in step S9. Thereafter, a confirmation message is sent to the customer in step S10 and the customer's account history is updated in step S11. In other words, the results of the customer interaction in step S9 are recorded in the collections system in step S11.
None of the known collection systems allow for an automated interactive session with the delinquent, overlimit or special status customer. Thus, all known collection systems must have human staff members to perform the customer interaction and receive customer responses and promises of payments. Thus, while VRU (Voice Response Units) and the system proposed by SolveMyDebt.com allow for the use of responses by punching keys on a telephone or keyboard, such input is limited to promises to pay and does not encompass interactive payment agreements or electronic methods for completing the agreements. Further, as set forth above, most systems utilize human operators to initiate customer contact. The present inventors have invented a system that enables a collection system to utilize modern electronic communication mediums to realize a fully automated collection system.